PolyFuzz | Fuzzy string matching , grouping , and evaluation | Natural Language Processing library
kandi X-RAY | PolyFuzz Summary
kandi X-RAY | PolyFuzz Summary
PolyFuzz performs fuzzy string matching, string grouping, and contains extensive evaluation functions. PolyFuzz is meant to bring fuzzy string matching techniques together within a single framework. Currently, methods include a variety of edit distance measures, a character-based n-gram TF-IDF, word embedding techniques such as FastText and GloVe, and transformers embeddings.
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Top functions reviewed by kandi - BETA
- Compute cosine similarity
- Compute matches based on the method
- Fit the model to the given list
- Embeds a list of strings
- Update model_ids
- Compute the top n values of a sparse matrix
- Compute the top n - n similarity score
- Compute cosine similarity between two vectors
- Visualize precision recall curve
- Compute the precision recall curve
- Check if the model is fit
- Visualize precision recall recall
- Calculate cosine similarity
- Compute matches from from_list
- Extract tfidf from from to_list
- Create a list of n - grams from a string
- Remove spaces and spaces
- Compute the cosine similarity between two documents
- Create logger
PolyFuzz Key Features
PolyFuzz Examples and Code Snippets
Community Discussions
Trending Discussions on PolyFuzz
QUESTION
I have a list of string and some are fragments of a longer word. I just want to keep the longest version of each word fragment.
In the following list I would like to keep the longest word 'indoor outdoor beanbag lounger'
and remove the other word fragments.
Example:
...ANSWER
Answered 2021-Aug-01 at 17:57As suggested by @atru, if all you need is the longest entry and all your entries have words separated by whitespace, then this simple code will solve your issue:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install PolyFuzz
For an in-depth overview of the possibilities of PolyFuzz you can check the full documentation here or you can follow along with the notebook here.
The main goal of PolyFuzz is to allow the user to perform different methods for matching strings. We start by defining two lists, one to map from and one to map to. We are going to be using TF-IDF to create n-grams on a character level in order to compare similarity between strings. Then, we calculate the similarity between strings by calculating the cosine similarity between vector representations.
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